Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations7253
Missing cells30498
Missing cells (%)23.4%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory3.6 MiB
Average record size in memory524.8 B

Variable types

Text3
Categorical5
Numeric7
Unsupported3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Brand is highly overall correlated with TransmissionHigh correlation
Car_Age is highly overall correlated with Kilometers_Driven and 2 other fieldsHigh correlation
Kilometers_Driven is highly overall correlated with Car_Age and 2 other fieldsHigh correlation
Kilometers_Driven_log is highly overall correlated with Car_Age and 2 other fieldsHigh correlation
Price is highly overall correlated with Price_log and 1 other fieldsHigh correlation
Price_log is highly overall correlated with Price and 1 other fieldsHigh correlation
Transmission is highly overall correlated with Brand and 2 other fieldsHigh correlation
Year is highly overall correlated with Car_Age and 2 other fieldsHigh correlation
Fuel_Type is highly imbalanced (53.6%) Imbalance
Owner_Type is highly imbalanced (61.0%) Imbalance
Mileage has 7253 (100.0%) missing values Missing
Engine has 7253 (100.0%) missing values Missing
Power has 7253 (100.0%) missing values Missing
New_Price has 6247 (86.1%) missing values Missing
Price has 1234 (17.0%) missing values Missing
Price_log has 1234 (17.0%) missing values Missing
Kilometers_Driven is highly skewed (γ1 = 61.58257466) Skewed
Mileage is an unsupported type, check if it needs cleaning or further analysis Unsupported
Engine is an unsupported type, check if it needs cleaning or further analysis Unsupported
Power is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-31 05:34:12.918866
Analysis finished2025-01-31 05:34:18.751464
Duration5.83 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Name
Text

Distinct2041
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size532.5 KiB
2025-01-31T11:04:18.994363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length48
Mean length26.15759
Min length11

Characters and Unicode

Total characters189721
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique862 ?
Unique (%)11.9%

Sample

1st rowMaruti Wagon R LXI CNG
2nd rowHyundai Creta 1.6 CRDi SX Option
3rd rowHonda Jazz V
4th rowMaruti Ertiga VDI
5th rowAudi A4 New 2.0 TDI Multitronic
ValueCountFrequency (%)
maruti 1444
 
4.1%
hyundai 1340
 
3.8%
honda 743
 
2.1%
at 655
 
1.9%
diesel 609
 
1.7%
1.2 521
 
1.5%
toyota 507
 
1.4%
tdi 479
 
1.4%
mt 419
 
1.2%
swift 418
 
1.2%
Other values (907) 27958
79.7%
2025-01-31T11:04:19.350926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97297
51.3%
Uppercase Letter 44507
23.5%
Space Separator 27840
 
14.7%
Decimal Number 15634
 
8.2%
Other Punctuation 2704
 
1.4%
Dash Punctuation 1401
 
0.7%
Open Punctuation 169
 
0.1%
Close Punctuation 169
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11809
12.1%
i 11426
11.7%
e 9233
9.5%
t 7952
8.2%
o 7950
8.2%
n 7822
8.0%
r 7647
 
7.9%
u 5214
 
5.4%
d 4930
 
5.1%
l 4156
 
4.3%
Other values (16) 19158
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 3941
 
8.9%
T 3922
 
8.8%
M 3654
 
8.2%
D 3536
 
7.9%
V 3220
 
7.2%
C 3080
 
6.9%
I 3025
 
6.8%
X 2484
 
5.6%
A 2430
 
5.5%
H 2419
 
5.4%
Other values (16) 12796
28.8%
Decimal Number
ValueCountFrequency (%)
0 3685
23.6%
2 3474
22.2%
1 3452
22.1%
5 1469
 
9.4%
4 962
 
6.2%
3 899
 
5.8%
8 585
 
3.7%
6 577
 
3.7%
7 364
 
2.3%
9 167
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 2678
99.0%
/ 22
 
0.8%
& 4
 
0.1%
Space Separator
ValueCountFrequency (%)
27840
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1401
100.0%
Open Punctuation
ValueCountFrequency (%)
( 169
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 141804
74.7%
Common 47917
 
25.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11809
 
8.3%
i 11426
 
8.1%
e 9233
 
6.5%
t 7952
 
5.6%
o 7950
 
5.6%
n 7822
 
5.5%
r 7647
 
5.4%
u 5214
 
3.7%
d 4930
 
3.5%
l 4156
 
2.9%
Other values (42) 63665
44.9%
Common
ValueCountFrequency (%)
27840
58.1%
0 3685
 
7.7%
2 3474
 
7.3%
1 3452
 
7.2%
. 2678
 
5.6%
5 1469
 
3.1%
- 1401
 
2.9%
4 962
 
2.0%
3 899
 
1.9%
8 585
 
1.2%
Other values (7) 1472
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Location
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size395.7 KiB
Mumbai
949 
Hyderabad
876 
Coimbatore
772 
Kochi
772 
Pune
765 
Other values (6)
3119 

Length

Max length10
Median length7
Mean length6.8470978
Min length4

Characters and Unicode

Total characters49662
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowPune
3rd rowChennai
4th rowChennai
5th rowCoimbatore

Common Values

ValueCountFrequency (%)
Mumbai 949
13.1%
Hyderabad 876
12.1%
Coimbatore 772
10.6%
Kochi 772
10.6%
Pune 765
10.5%
Delhi 660
9.1%
Kolkata 654
9.0%
Chennai 591
8.1%
Jaipur 499
6.9%
Bangalore 440
6.1%

Length

2025-01-31T11:04:19.424994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 949
13.1%
hyderabad 876
12.1%
coimbatore 772
10.6%
kochi 772
10.6%
pune 765
10.5%
delhi 660
9.1%
kolkata 654
9.0%
chennai 591
8.1%
jaipur 499
6.9%
bangalore 440
6.1%

Most occurring characters

ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42409
85.4%
Uppercase Letter 7253
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7301
17.2%
e 4379
10.3%
i 4243
10.0%
o 3410
8.0%
b 2872
 
6.8%
r 2587
 
6.1%
n 2387
 
5.6%
d 2302
 
5.4%
h 2298
 
5.4%
u 2213
 
5.2%
Other values (8) 8417
19.8%
Uppercase Letter
ValueCountFrequency (%)
K 1426
19.7%
C 1363
18.8%
M 949
13.1%
H 876
12.1%
P 765
10.5%
D 660
9.1%
J 499
 
6.9%
B 440
 
6.1%
A 275
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 49662
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Year
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3654
Minimum1996
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:19.499995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2007
Q12011
median2014
Q32016
95-th percentile2018
Maximum2019
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2544208
Coefficient of variation (CV)0.0016164085
Kurtosis0.91048764
Mean2013.3654
Median Absolute Deviation (MAD)2
Skewness-0.83981615
Sum14602939
Variance10.591255
MonotonicityNot monotonic
2025-01-31T11:04:19.573787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2015 929
12.8%
2014 925
12.8%
2016 886
12.2%
2013 791
10.9%
2017 709
9.8%
2012 690
9.5%
2011 579
8.0%
2010 407
5.6%
2018 361
 
5.0%
2009 252
 
3.5%
Other values (13) 724
10.0%
ValueCountFrequency (%)
1996 1
 
< 0.1%
1998 4
 
0.1%
1999 2
 
< 0.1%
2000 5
 
0.1%
2001 8
 
0.1%
2002 18
 
0.2%
2003 20
 
0.3%
2004 35
 
0.5%
2005 68
0.9%
2006 89
1.2%
ValueCountFrequency (%)
2019 119
 
1.6%
2018 361
 
5.0%
2017 709
9.8%
2016 886
12.2%
2015 929
12.8%
2014 925
12.8%
2013 791
10.9%
2012 690
9.5%
2011 579
8.0%
2010 407
5.6%

Kilometers_Driven
Real number (ℝ)

High correlation  Skewed 

Distinct3660
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58699.063
Minimum171
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:19.672474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile13017.6
Q134000
median53416
Q373000
95-th percentile120241.8
Maximum6500000
Range6499829
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation84427.721
Coefficient of variation (CV)1.4383146
Kurtosis4674.734
Mean58699.063
Median Absolute Deviation (MAD)19584
Skewness61.582575
Sum4.257443 × 108
Variance7.12804 × 109
MonotonicityNot monotonic
2025-01-31T11:04:19.772015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 96
 
1.3%
65000 86
 
1.2%
45000 86
 
1.2%
70000 77
 
1.1%
50000 72
 
1.0%
55000 66
 
0.9%
75000 64
 
0.9%
52000 62
 
0.9%
35000 62
 
0.9%
80000 62
 
0.9%
Other values (3650) 6520
89.9%
ValueCountFrequency (%)
171 1
 
< 0.1%
600 1
 
< 0.1%
1000 11
0.2%
1001 4
 
0.1%
1011 1
 
< 0.1%
1015 1
 
< 0.1%
1048 1
 
< 0.1%
1261 1
 
< 0.1%
1331 1
 
< 0.1%
1400 1
 
< 0.1%
ValueCountFrequency (%)
6500000 1
< 0.1%
775000 1
< 0.1%
720000 1
< 0.1%
620000 1
< 0.1%
480000 2
< 0.1%
445000 1
< 0.1%
350000 1
< 0.1%
300000 1
< 0.1%
299322 1
< 0.1%
290000 1
< 0.1%

Fuel_Type
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
Diesel
3852 
Petrol
3325 
CNG
 
62
LPG
 
12
Electric
 
2

Length

Max length8
Median length6
Mean length5.9699435
Min length3

Characters and Unicode

Total characters43300
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel 3852
53.1%
Petrol 3325
45.8%
CNG 62
 
0.9%
LPG 12
 
0.2%
Electric 2
 
< 0.1%

Length

2025-01-31T11:04:19.885065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T11:04:19.951653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3852
53.1%
petrol 3325
45.8%
cng 62
 
0.9%
lpg 12
 
0.2%
electric 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35899
82.9%
Uppercase Letter 7401
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11031
30.7%
l 7179
20.0%
i 3854
 
10.7%
s 3852
 
10.7%
t 3327
 
9.3%
r 3327
 
9.3%
o 3325
 
9.3%
c 4
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D 3852
52.0%
P 3337
45.1%
G 74
 
1.0%
C 62
 
0.8%
N 62
 
0.8%
L 12
 
0.2%
E 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 43300
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size395.7 KiB
Manual
5204 
Automatic
2049 

Length

Max length9
Median length6
Mean length6.8475114
Min length6

Characters and Unicode

Total characters49665
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual 5204
71.7%
Automatic 2049
 
28.3%

Length

2025-01-31T11:04:20.071430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T11:04:20.143993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 5204
71.7%
automatic 2049
 
28.3%

Most occurring characters

ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42412
85.4%
Uppercase Letter 7253
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12457
29.4%
u 7253
17.1%
n 5204
12.3%
l 5204
12.3%
t 4098
 
9.7%
o 2049
 
4.8%
m 2049
 
4.8%
i 2049
 
4.8%
c 2049
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
M 5204
71.7%
A 2049
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 49665
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Owner_Type
Categorical

Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size383.8 KiB
First
5952 
Second
1152 
Third
 
137
Fourth & Above
 
12

Length

Max length14
Median length5
Mean length5.1737212
Min length5

Characters and Unicode

Total characters37525
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst
2nd rowFirst
3rd rowFirst
4th rowFirst
5th rowSecond

Common Values

ValueCountFrequency (%)
First 5952
82.1%
Second 1152
 
15.9%
Third 137
 
1.9%
Fourth & Above 12
 
0.2%

Length

2025-01-31T11:04:20.223618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-31T11:04:20.329273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first 5952
81.8%
second 1152
 
15.8%
third 137
 
1.9%
fourth 12
 
0.2%
12
 
0.2%
above 12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
n 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30224
80.5%
Uppercase Letter 7265
 
19.4%
Space Separator 24
 
0.1%
Other Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 6101
20.2%
i 6089
20.1%
t 5964
19.7%
s 5952
19.7%
d 1289
 
4.3%
o 1176
 
3.9%
e 1164
 
3.9%
n 1152
 
3.8%
c 1152
 
3.8%
h 149
 
0.5%
Other values (3) 36
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 5964
82.1%
S 1152
 
15.9%
T 137
 
1.9%
A 12
 
0.2%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
& 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37489
99.9%
Common 36
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
n 1152
 
3.1%
c 1152
 
3.1%
Other values (7) 1486
 
4.0%
Common
ValueCountFrequency (%)
24
66.7%
& 12
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
n 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Mileage
Unsupported

Missing  Rejected  Unsupported 

Missing7253
Missing (%)100.0%
Memory size56.8 KiB

Engine
Unsupported

Missing  Rejected  Unsupported 

Missing7253
Missing (%)100.0%
Memory size56.8 KiB

Power
Unsupported

Missing  Rejected  Unsupported 

Missing7253
Missing (%)100.0%
Memory size56.8 KiB

Seats
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing23
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.2791148
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:20.381368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81066572
Coefficient of variation (CV)0.15356092
Kurtosis4.7205148
Mean5.2791148
Median Absolute Deviation (MAD)0
Skewness1.9060758
Sum38168
Variance0.65717891
MonotonicityNot monotonic
2025-01-31T11:04:20.463316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 6075
83.8%
7 798
 
11.0%
8 170
 
2.3%
4 119
 
1.6%
6 38
 
0.5%
2 18
 
0.2%
10 8
 
0.1%
9 3
 
< 0.1%
0 1
 
< 0.1%
(Missing) 23
 
0.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 18
 
0.2%
4 119
 
1.6%
5 6075
83.8%
6 38
 
0.5%
7 798
 
11.0%
8 170
 
2.3%
9 3
 
< 0.1%
10 8
 
0.1%
ValueCountFrequency (%)
10 8
 
0.1%
9 3
 
< 0.1%
8 170
 
2.3%
7 798
 
11.0%
6 38
 
0.5%
5 6075
83.8%
4 119
 
1.6%
2 18
 
0.2%
0 1
 
< 0.1%

New_Price
Text

Missing 

Distinct625
Distinct (%)62.1%
Missing6247
Missing (%)86.1%
Memory size252.7 KiB
2025-01-31T11:04:20.676478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4194831
Min length4

Characters and Unicode

Total characters9476
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391 ?
Unique (%)38.9%

Sample

1st row8.61 Lakh
2nd row21 Lakh
3rd row10.65 Lakh
4th row32.01 Lakh
5th row47.87 Lakh
ValueCountFrequency (%)
lakh 986
49.0%
cr 20
 
1.0%
33.36 6
 
0.3%
95.13 6
 
0.3%
4.78 6
 
0.3%
63.71 6
 
0.3%
15.05 5
 
0.2%
11.48 5
 
0.2%
4.98 5
 
0.2%
11.26 5
 
0.2%
Other values (617) 962
47.8%
2025-01-31T11:04:20.964627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
L 986
10.4%
a 986
10.4%
k 986
10.4%
h 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3497
36.9%
Lowercase Letter 2978
31.4%
Space Separator 1006
 
10.6%
Uppercase Letter 1006
 
10.6%
Other Punctuation 989
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 627
17.9%
4 386
11.0%
5 371
10.6%
7 356
10.2%
6 345
9.9%
2 325
9.3%
8 310
8.9%
3 307
8.8%
9 290
8.3%
0 180
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
a 986
33.1%
k 986
33.1%
h 986
33.1%
r 20
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
L 986
98.0%
C 20
 
2.0%
Space Separator
ValueCountFrequency (%)
1006
100.0%
Other Punctuation
ValueCountFrequency (%)
. 989
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5492
58.0%
Latin 3984
42.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1006
18.3%
. 989
18.0%
1 627
11.4%
4 386
 
7.0%
5 371
 
6.8%
7 356
 
6.5%
6 345
 
6.3%
2 325
 
5.9%
8 310
 
5.6%
3 307
 
5.6%
Other values (2) 470
8.6%
Latin
ValueCountFrequency (%)
L 986
24.7%
a 986
24.7%
k 986
24.7%
h 986
24.7%
C 20
 
0.5%
r 20
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
L 986
10.4%
a 986
10.4%
k 986
10.4%
h 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Price
Real number (ℝ)

High correlation  Missing 

Distinct1373
Distinct (%)22.8%
Missing1234
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean9.4794684
Minimum0.44
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:21.050478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile1.7
Q13.5
median5.64
Q39.95
95-th percentile32.446
Maximum160
Range159.56
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation11.187917
Coefficient of variation (CV)1.1802262
Kurtosis17.092202
Mean9.4794684
Median Absolute Deviation (MAD)2.62
Skewness3.335232
Sum57056.92
Variance125.16949
MonotonicityNot monotonic
2025-01-31T11:04:21.136050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 88
 
1.2%
5.5 84
 
1.2%
3.5 82
 
1.1%
4.25 73
 
1.0%
3.25 71
 
1.0%
3 68
 
0.9%
6.5 64
 
0.9%
2.5 63
 
0.9%
4 56
 
0.8%
4.75 53
 
0.7%
Other values (1363) 5317
73.3%
(Missing) 1234
 
17.0%
ValueCountFrequency (%)
0.44 1
 
< 0.1%
0.45 3
< 0.1%
0.5 2
< 0.1%
0.51 1
 
< 0.1%
0.53 2
< 0.1%
0.55 3
< 0.1%
0.6 2
< 0.1%
0.63 1
 
< 0.1%
0.65 2
< 0.1%
0.69 1
 
< 0.1%
ValueCountFrequency (%)
160 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
97.07 1
< 0.1%
93.67 1
< 0.1%
93 1
< 0.1%
90 1
< 0.1%
85 1
< 0.1%
83.96 1
< 0.1%
79 2
< 0.1%

Car_Age
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.634634
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:21.219182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q19
median11
Q314
95-th percentile18
Maximum29
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2544208
Coefficient of variation (CV)0.27971837
Kurtosis0.91048764
Mean11.634634
Median Absolute Deviation (MAD)2
Skewness0.83981615
Sum84386
Variance10.591255
MonotonicityNot monotonic
2025-01-31T11:04:21.298907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 929
12.8%
11 925
12.8%
9 886
12.2%
12 791
10.9%
8 709
9.8%
13 690
9.5%
14 579
8.0%
15 407
5.6%
7 361
 
5.0%
16 252
 
3.5%
Other values (13) 724
10.0%
ValueCountFrequency (%)
6 119
 
1.6%
7 361
 
5.0%
8 709
9.8%
9 886
12.2%
10 929
12.8%
11 925
12.8%
12 791
10.9%
13 690
9.5%
14 579
8.0%
15 407
5.6%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 4
 
0.1%
26 2
 
< 0.1%
25 5
 
0.1%
24 8
 
0.1%
23 18
 
0.2%
22 20
 
0.3%
21 35
 
0.5%
20 68
0.9%
19 89
1.2%

Brand
Categorical

High correlation 

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size393.2 KiB
Maruti
1444 
Hyundai
1340 
Honda
743 
Toyota
507 
Mercedes-Benz
380 
Other values (27)
2839 

Length

Max length13
Median length11
Mean length6.4996553
Min length3

Characters and Unicode

Total characters47142
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowMaruti
2nd rowHyundai
3rd rowHonda
4th rowMaruti
5th rowAudi

Common Values

ValueCountFrequency (%)
Maruti 1444
19.9%
Hyundai 1340
18.5%
Honda 743
10.2%
Toyota 507
 
7.0%
Mercedes-Benz 380
 
5.2%
Volkswagen 374
 
5.2%
Ford 351
 
4.8%
Mahindra 331
 
4.6%
BMW 312
 
4.3%
Audi 285
 
3.9%
Other values (22) 1186
16.4%

Length

2025-01-31T11:04:21.371624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti 1444
19.6%
hyundai 1340
18.2%
honda 743
10.1%
toyota 507
 
6.9%
mercedes-benz 380
 
5.2%
volkswagen 374
 
5.1%
ford 351
 
4.8%
mahindra 331
 
4.5%
bmw 312
 
4.2%
audi 285
 
3.9%
Other values (24) 1284
17.5%

Most occurring characters

ValueCountFrequency (%)
a 6239
13.2%
i 3728
 
7.9%
d 3701
 
7.9%
n 3575
 
7.6%
u 3351
 
7.1%
o 3045
 
6.5%
r 2829
 
6.0%
t 2595
 
5.5%
M 2534
 
5.4%
e 2529
 
5.4%
Other values (32) 13016
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38308
81.3%
Uppercase Letter 8356
 
17.7%
Dash Punctuation 380
 
0.8%
Space Separator 98
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6239
16.3%
i 3728
9.7%
d 3701
9.7%
n 3575
9.3%
u 3351
8.7%
o 3045
7.9%
r 2829
7.4%
t 2595
6.8%
e 2529
6.6%
y 1849
 
4.8%
Other values (12) 4867
12.7%
Uppercase Letter
ValueCountFrequency (%)
M 2534
30.3%
H 2084
24.9%
T 735
 
8.8%
B 694
 
8.3%
V 402
 
4.8%
F 392
 
4.7%
W 312
 
3.7%
A 286
 
3.4%
R 237
 
2.8%
S 203
 
2.4%
Other values (8) 477
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 380
100.0%
Space Separator
ValueCountFrequency (%)
98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46664
99.0%
Common 478
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6239
13.4%
i 3728
 
8.0%
d 3701
 
7.9%
n 3575
 
7.7%
u 3351
 
7.2%
o 3045
 
6.5%
r 2829
 
6.1%
t 2595
 
5.6%
M 2534
 
5.4%
e 2529
 
5.4%
Other values (30) 12538
26.9%
Common
ValueCountFrequency (%)
- 380
79.5%
98
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6239
13.2%
i 3728
 
7.9%
d 3701
 
7.9%
n 3575
 
7.6%
u 3351
 
7.1%
o 3045
 
6.5%
r 2829
 
6.0%
t 2595
 
5.5%
M 2534
 
5.4%
e 2529
 
5.4%
Other values (32) 13016
27.6%

Model
Text

Distinct726
Distinct (%)10.0%
Missing1
Missing (%)< 0.1%
Memory size408.1 KiB
2025-01-31T11:04:21.493166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length15
Mean length8.5990072
Min length3

Characters and Unicode

Total characters62360
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)2.6%

Sample

1st rowWagonR
2nd rowCreta1.6
3rd rowJazzV
4th rowErtigaVDI
5th rowA4New
ValueCountFrequency (%)
swiftdzire 189
 
2.6%
grandi10 179
 
2.5%
wagonr 178
 
2.5%
innova2.5 145
 
2.0%
verna1.6 127
 
1.8%
city1.5 122
 
1.7%
cityi 115
 
1.6%
creta1.6 110
 
1.5%
newc-class 110
 
1.5%
3series 109
 
1.5%
Other values (695) 5868
80.9%
2025-01-31T11:04:21.727904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4369
 
7.0%
a 3887
 
6.2%
e 3808
 
6.1%
o 3372
 
5.4%
r 3307
 
5.3%
t 3134
 
5.0%
0 2514
 
4.0%
n 2509
 
4.0%
1 2339
 
3.8%
s 1991
 
3.2%
Other values (58) 31130
49.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35330
56.7%
Uppercase Letter 14825
23.8%
Decimal Number 9801
 
15.7%
Other Punctuation 1703
 
2.7%
Dash Punctuation 699
 
1.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4369
12.4%
a 3887
11.0%
e 3808
10.8%
o 3372
9.5%
r 3307
9.4%
t 3134
8.9%
n 2509
7.1%
s 1991
 
5.6%
l 1792
 
5.1%
z 945
 
2.7%
Other values (16) 6216
17.6%
Uppercase Letter
ValueCountFrequency (%)
S 1767
11.9%
C 1626
11.0%
V 1292
 
8.7%
D 1162
 
7.8%
X 1090
 
7.4%
A 960
 
6.5%
E 947
 
6.4%
I 746
 
5.0%
R 636
 
4.3%
L 633
 
4.3%
Other values (16) 3966
26.8%
Decimal Number
ValueCountFrequency (%)
0 2514
25.7%
1 2339
23.9%
2 1844
18.8%
5 1087
11.1%
3 526
 
5.4%
4 497
 
5.1%
6 466
 
4.8%
8 292
 
3.0%
7 139
 
1.4%
9 97
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 1698
99.7%
& 4
 
0.2%
/ 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 699
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50155
80.4%
Common 12205
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4369
 
8.7%
a 3887
 
7.7%
e 3808
 
7.6%
o 3372
 
6.7%
r 3307
 
6.6%
t 3134
 
6.2%
n 2509
 
5.0%
s 1991
 
4.0%
l 1792
 
3.6%
S 1767
 
3.5%
Other values (42) 20219
40.3%
Common
ValueCountFrequency (%)
0 2514
20.6%
1 2339
19.2%
2 1844
15.1%
. 1698
13.9%
5 1087
8.9%
- 699
 
5.7%
3 526
 
4.3%
4 497
 
4.1%
6 466
 
3.8%
8 292
 
2.4%
Other values (6) 243
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4369
 
7.0%
a 3887
 
6.2%
e 3808
 
6.1%
o 3372
 
5.4%
r 3307
 
5.3%
t 3134
 
5.0%
0 2514
 
4.0%
n 2509
 
4.0%
1 2339
 
3.8%
s 1991
 
3.2%
Other values (58) 31130
49.9%

Kilometers_Driven_log
Real number (ℝ)

High correlation 

Distinct3660
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.760978
Minimum5.1416636
Maximum15.687313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-01-31T11:04:21.809914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.1416636
5-th percentile9.4740574
Q110.434116
median10.885866
Q311.198215
95-th percentile11.69726
Maximum15.687313
Range10.545649
Interquartile range (IQR)0.76409892

Descriptive statistics

Standard deviation0.71632692
Coefficient of variation (CV)0.066567084
Kurtosis4.58503
Mean10.760978
Median Absolute Deviation (MAD)0.3680847
Skewness-1.2987715
Sum78049.373
Variance0.51312426
MonotonicityNot monotonic
2025-01-31T11:04:21.899908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.00209984 96
 
1.3%
11.08214255 86
 
1.2%
10.71441777 86
 
1.2%
11.15625052 77
 
1.1%
10.81977828 72
 
1.0%
10.91508846 66
 
0.9%
11.22524339 64
 
0.9%
10.858999 62
 
0.9%
10.46310334 62
 
0.9%
11.28978191 62
 
0.9%
Other values (3650) 6520
89.9%
ValueCountFrequency (%)
5.141663557 1
 
< 0.1%
6.396929655 1
 
< 0.1%
6.907755279 11
0.2%
6.908754779 4
 
0.1%
6.918695219 1
 
< 0.1%
6.922643891 1
 
< 0.1%
6.954638865 1
 
< 0.1%
7.139660336 1
 
< 0.1%
7.193685818 1
 
< 0.1%
7.244227516 1
 
< 0.1%
ValueCountFrequency (%)
15.68731273 1
< 0.1%
13.56061831 1
< 0.1%
13.48700649 1
< 0.1%
13.33747476 1
< 0.1%
13.08154138 2
< 0.1%
13.00582956 1
< 0.1%
12.76568843 1
< 0.1%
12.61153775 1
< 0.1%
12.6092752 1
< 0.1%
12.5776362 1
< 0.1%

Price_log
Real number (ℝ)

High correlation  Missing 

Distinct1373
Distinct (%)22.8%
Missing1234
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean1.8250951
Minimum-0.82098055
Maximum5.0751738
Zeros15
Zeros (%)0.2%
Negative77
Negative (%)1.1%
Memory size56.8 KiB
2025-01-31T11:04:22.171899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.82098055
5-th percentile0.53062825
Q11.252763
median1.7298841
Q32.2975726
95-th percentile3.479577
Maximum5.0751738
Range5.8961544
Interquartile range (IQR)1.0448096

Descriptive statistics

Standard deviation0.8740586
Coefficient of variation (CV)0.47891126
Kurtosis0.17272636
Mean1.8250951
Median Absolute Deviation (MAD)0.51613068
Skewness0.41739069
Sum10985.248
Variance0.76397844
MonotonicityNot monotonic
2025-01-31T11:04:22.258792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.504077397 88
 
1.2%
1.704748092 84
 
1.2%
1.252762968 82
 
1.1%
1.446918983 73
 
1.0%
1.178654996 71
 
1.0%
1.098612289 68
 
0.9%
1.871802177 64
 
0.9%
0.9162907319 63
 
0.9%
1.386294361 56
 
0.8%
1.558144618 53
 
0.7%
Other values (1363) 5317
73.3%
(Missing) 1234
 
17.0%
ValueCountFrequency (%)
-0.8209805521 1
 
< 0.1%
-0.7985076962 3
< 0.1%
-0.6931471806 2
< 0.1%
-0.6733445533 1
 
< 0.1%
-0.6348782724 2
< 0.1%
-0.5978370008 3
< 0.1%
-0.5108256238 2
< 0.1%
-0.4620354596 1
 
< 0.1%
-0.4307829161 2
< 0.1%
-0.3710636814 1
 
< 0.1%
ValueCountFrequency (%)
5.075173815 1
< 0.1%
4.787491743 1
< 0.1%
4.605170186 1
< 0.1%
4.575432368 1
< 0.1%
4.539777967 1
< 0.1%
4.532599493 1
< 0.1%
4.49980967 1
< 0.1%
4.442651256 1
< 0.1%
4.430340495 1
< 0.1%
4.369447852 2
< 0.1%

Interactions

2025-01-31T11:04:17.537698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:13.931862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.465358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.151689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.813388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.406852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.957264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.779246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.009862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.535418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.243241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.895951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.492378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.027973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.882084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.077456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.732963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.318256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.996306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.570974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.107730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.964246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.154253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.817207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.390249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.078367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.645366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.181527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:18.049225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.229723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.902693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.478819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.155937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.732943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.265878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:18.119767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.315784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.981246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.597810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.247837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.808930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.354470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:18.192800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:14.388805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.073271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:15.708025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.325539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:16.881465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-31T11:04:17.438544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-31T11:04:22.338573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BrandCar_AgeFuel_TypeKilometers_DrivenKilometers_Driven_logLocationOwner_TypePricePrice_logSeatsTransmissionYear
Brand1.0000.1770.2440.0000.1130.0840.0840.4650.3480.3460.6990.177
Car_Age0.1771.0000.0950.5520.5520.1360.251-0.491-0.491-0.0330.101-1.000
Fuel_Type0.2440.0951.0000.0000.1170.0930.0230.1480.2220.1600.1500.095
Kilometers_Driven0.0000.5520.0001.0001.0000.0080.000-0.215-0.2150.1900.012-0.552
Kilometers_Driven_log0.1130.5520.1171.0001.0000.1210.124-0.215-0.2150.1900.099-0.552
Location0.0840.1360.0930.0080.1211.0000.1600.0740.1210.0250.1950.136
Owner_Type0.0840.2510.0230.0000.1240.1601.0000.0580.1730.0460.0070.251
Price0.465-0.4910.148-0.215-0.2150.0740.0581.0001.0000.2210.5950.491
Price_log0.348-0.4910.222-0.215-0.2150.1210.1731.0001.0000.2210.6610.491
Seats0.346-0.0330.1600.1900.1900.0250.0460.2210.2211.0000.1460.033
Transmission0.6990.1010.1500.0120.0990.1950.0070.5950.6610.1461.0000.101
Year0.177-1.0000.095-0.552-0.5520.1360.2510.4910.4910.0330.1011.000

Missing values

2025-01-31T11:04:18.367868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-31T11:04:18.535133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-31T11:04:18.684694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePriceCar_AgeBrandModelKilometers_Driven_logPrice_log
0Maruti Wagon R LXI CNGMumbai201072000CNGManualFirstNaNNaNNaN5.0NaN1.7515MarutiWagonR11.1844210.559616
1Hyundai Creta 1.6 CRDi SX OptionPune201541000DieselManualFirstNaNNaNNaN5.0NaN12.5010HyundaiCreta1.610.6213272.525729
2Honda Jazz VChennai201146000PetrolManualFirstNaNNaNNaN5.08.61 Lakh4.5014HondaJazzV10.7363971.504077
3Maruti Ertiga VDIChennai201287000DieselManualFirstNaNNaNNaN7.0NaN6.0013MarutiErtigaVDI11.3736631.791759
4Audi A4 New 2.0 TDI MultitronicCoimbatore201340670DieselAutomaticSecondNaNNaNNaN5.0NaN17.7412AudiA4New10.6132462.875822
5Hyundai EON LPG Era Plus OptionHyderabad201275000LPGManualFirstNaNNaNNaN5.0NaN2.3513HyundaiEONLPG11.2252430.854415
6Nissan Micra Diesel XVJaipur201386999DieselManualFirstNaNNaNNaN5.0NaN3.5012NissanMicraDiesel11.3736521.252763
7Toyota Innova Crysta 2.8 GX AT 8SMumbai201636000DieselAutomaticFirstNaNNaNNaN8.021 Lakh17.509ToyotaInnovaCrysta10.4912742.862201
8Volkswagen Vento Diesel ComfortlinePune201364430DieselManualFirstNaNNaNNaN5.0NaN5.2012VolkswagenVentoDiesel11.0733351.648659
9Tata Indica Vista Quadrajet LSChennai201265932DieselManualSecondNaNNaNNaN5.0NaN1.9513TataIndicaVista11.0963790.667829
NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePriceCar_AgeBrandModelKilometers_Driven_logPrice_log
7243Renault Duster 85PS Diesel RxLChennai201570000DieselManualFirstNaNNaNNaN5.0NaNNaN10RenaultDuster85PS11.156251NaN
7244Chevrolet Aveo 1.4 LSPune200945463PetrolManualFirstNaNNaNNaN5.0NaNNaN16ChevroletAveo1.410.724654NaN
7245Honda Amaze S i-VtechKochi201544776PetrolManualFirstNaNNaNNaN5.0NaNNaN10HondaAmazeS10.709428NaN
7246Hyundai Grand i10 AT AstaCoimbatore201618242PetrolAutomaticFirstNaNNaNNaN5.0NaNNaN9HyundaiGrandi109.811482NaN
7247Hyundai EON D Lite PlusCoimbatore201521190PetrolManualFirstNaNNaNNaN5.0NaNNaN10HyundaiEOND9.961285NaN
7248Volkswagen Vento Diesel TrendlineHyderabad201189411DieselManualFirstNaNNaNNaN5.0NaNNaN14VolkswagenVentoDiesel11.400999NaN
7249Volkswagen Polo GT TSIMumbai201559000PetrolAutomaticFirstNaNNaNNaN5.0NaNNaN10VolkswagenPoloGT10.985293NaN
7250Nissan Micra Diesel XVKolkata201228000DieselManualFirstNaNNaNNaN5.0NaNNaN13NissanMicraDiesel10.239960NaN
7251Volkswagen Polo GT TSIPune201352262PetrolAutomaticThirdNaNNaNNaN5.0NaNNaN12VolkswagenPoloGT10.864025NaN
7252Mercedes-Benz E-Class 2009-2013 E 220 CDI AvantgardeKochi201472443DieselAutomaticFirstNaNNaNNaN5.0NaNNaN11Mercedes-BenzE-Class2009-201311.190555NaN

Duplicate rows

Most frequently occurring

NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeSeatsNew_PricePriceCar_AgeBrandModelKilometers_Driven_logPrice_log# duplicates
0Honda City 1.5 E MTMumbai201052000PetrolManualFirst5.0NaNNaN15HondaCity1.510.858999NaN2